visualize_pvals {HiCcompare} | R Documentation

## Function to visualize p-values from HiCcompare results

### Description

Function to visualize p-values from HiCcompare results

### Usage

    visualize_pvals(hic.table, alpha = NA, adj.p = TRUE)

### Arguments

`hic.table` | A hic.table object that has been normalized and has had differences detected.
`alpha` | The alpha level at which you will call a p-value significant. If this is set to a numeric value then any p-values >= alpha will be set to 1 for the visualization in the heatmap. Defaults to NA for visualization of all p-values.
`adj.p` | Logical, Should the multiple testing corrected p-values be used (TRUE) or the raw p-values (FALSE)?

### Details

The goal of this function is to visualize where in the Hi-C matrix the differences are occuring between two experimental conditions. The function will produce a heatmap of the -log10(p-values) * sign(adj.M) to visualize where the significant differences between the datasets are occuring on the genome.

### Value

A heatmap

### Examples

    # Create hic.table object using included Hi-C data in sparse upper triangular
    # matrix format
    data('HMEC.chr22')
    data('NHEK.chr22')
    hic.table <- create.hic.table(HMEC.chr22, NHEK.chr22, chr = 'chr22')
    # Plug hic.table into hic_loess()
    result <- hic_loess(hic.table, Plot = TRUE)
    # perform difference detection
    diff.result <- hic_compare(result, Plot = TRUE)
    # visualize p-values
    visualize_pvals(diff.result)

[Package HiCcompare version 1.8.0 Index]